activity
20182022
most citedNeuromorphic Hardware learns to learn

55 citations · 61 across the 4 of their papers we have counts for

collaborators

8 papers

cs.NE2022

A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware

Eric Müller, Elias Arnold, Oliver Breitwieser +20

Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This wor…

cs.NE20222 cited

The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity

Christian Pehle, Sebastian Billaudelle, Benjamin Cramer +7

Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives. Brain-inspired…

cond-mat.dis-nn2020

Neuromorphic quantum computing

Christian Pehle, Christof Wetterich

We propose that neuromorphic computing can perform quantum operations. Spiking neurons in the active or silent states are connected to the two states of Ising spins. A quantum dens…

q-bio.NC20204 cited

Closed-loop experiments on the BrainScaleS-2 architecture

K. Schreiber, T. C. Wunderlich, C. Pehle +3

The evolution of biological brains has always been contingent on their embodiment within their respective environments, in which survival required appropriate navigation and manipu…

cs.NE201955 cited

Neuromorphic Hardware learns to learn

Thomas Bohnstingl, Franz Scherr, Christian Pehle +2

Hyperparameters and learning algorithms for neuromorphic hardware are usually chosen by hand. In contrast, the hyperparameters and learning algorithms of networks of neurons in the…

cs.NE2018

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Timo Wunderlich, Akos F. Kungl, Eric Müller +14

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of com…